Land Use and Land Cover Change Within and Around the Greater Serengeti Ecosystem, Tanzania

Kija, Hamza; Ogutu, Joseph O.; Mangewa, Lazaro J.; Bukombe, John; Verones, Francesca; Graae, Bente Jessen; Kideghesho, Jafari R.; Said, Mohammed Yahya; Nzunda, Emmanuel F.

doi:10.11648/j.ajrs.20200801.11

Cited by 8 publications

(4 citation statements)

References 85 publications

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“…The slight increment of 51 ha of forest area between 1999 and 2009 was likely due to a compensatory afforestation and plantation program (Mbane et al, 2019). The findings that a large proportion of natural vegetation (152 656 ha in 1989 to 116 382 ha in 2019 or 79% in 1989 to 60% in 2019) was transformed to farmland and settlements are consistent with other studies in eastern Africa (Bullock et al, 2021), indicating that anthropogenic activities are the main driver for LULC changes (Kija et al, 2020;. The total human population of the EWMA was around 47 103 people in 2012, with an average annual growth rate of 3% (Kulindwa et al, 2003), which might have led to an increased demand for natural resources and land.…”

Section: Management Areasupporting

confidence: 88%

“…Bushland and woodland cover did not change, while bare ground declined slightly over time from 2009 to 2019 (Table 3). Furthermore, results showed a high rate of agreement between the user's accuracy and producer's accuracy in terms of grassland, woodland, and water cover changes across all images with Kappa Indices of Agreement of 0.86, 0.87, 0.79 and 0.91 for the periods under investigation, which is similar to the standard land cover mapping accuracy (Kija et al, 2020) of 85-90%. Above 0.75 Kappa is the minimum acceptable interrater agreement (McHugh, 2012).…”

Section: Land Use / Land Cover Changessupporting

confidence: 60%

“…2). The corridor is, however, under threat following the expansion of human activities in the area and the changes in land use over the years (Kija et al, 2020). The human population in EWMA is about 57 103 people, having increased by 30% between 1988 and 2017 (Dekker, 2018).…”

Section: Description Of the Study Areamentioning

confidence: 99%

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Human-elephant interactions: exploring conflicts and drivers in enduimet wildlife management area, Tanzania

Sanare,

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A globally rapid land use/land cover change in human-transformed landscapes alters the interface of human-wildlife interactions due to shifting socio-ecological and environmental pressures. Understanding these shifts is crucial for mitigating repeated negative interactions that escalate conflict states between people and wildlife. This study aimed to understand land use/land cover change changes between1989–2019, with more recent spatio-temporal patterns of high pressure at the human-elephant interface, and potentially underlying environmental and human driven factors that affect elephant movement patterns. The study analyzed a dataset of 923 human-elephant conflict occurrences, mainly crop foraging incidents in the Enduimet between the years 2016 and 2020 and combined these data with land use/land cover change for year 2019 to understand potential drivers of conflict. Furthermore, GPS datasets of elephants collared between 2019 to 2020 used to understand elephant movement patterns in changing land use types. Landsat image study revealed that 41% of the area had been converted into farmlands and settlements within the last three decades, which creates elephant-intolerant habitats and the potential to increase pressure at the human elephant interface. The collared elephants using Enduimet moved through all land use types and did not avoid settlements, although they moved through these at higher speeds, reflecting perception of risk. Elephants travelled slightly more slowly in farmland, likely reflecting the availability of foraging opportunities. Conclusively, communities in land use/land cover change urgently need support to increase the effective distance between their farming activities and the protected areas.

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Section: Management Areasupporting

confidence: 88%

Section: Land Use / Land Cover Changessupporting

confidence: 60%

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Human-elephant interactions: exploring conflicts and drivers in enduimet wildlife management area, Tanzania

Sanare,

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“…RF was implemented using the 'SuperClass' function, which takes as an input, the training data and the corrected Landsat image [58]. Specifically in African savannahs, RF classification has successfully been applied in southern Africa [29,[36][37][38] and eastern Africa [62,63].…”

Section: Training Samples and Classificationmentioning

confidence: 99%

Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons

et al. 2020

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Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration.

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Landsat time series reveal forest loss and woody encroachment in the Ngorongoro Conservation Area, Tanzania

Borges

Higginbottom

Cain

et al. 2022

Remote Sens Ecol Conserv

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Land Use and Land Cover Change Within and Around the Greater Serengeti Ecosystem, Tanzania

Cited by 8 publications

References 85 publications

Human-elephant interactions: exploring conflicts and drivers in enduimet wildlife management area, Tanzania

Human-elephant interactions: exploring conflicts and drivers in enduimet wildlife management area, Tanzania

Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons

Landsat time series reveal forest loss and woody encroachment in the Ngorongoro Conservation Area, Tanzania

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